Welcome to my Fourth Quarterly Hockey Betting Report of the 2021/22
season. Unlike my weekly reports, the quarterly report delves deeper into my
team-by-team results. It should be noted that I’m not betting with real money. These are
all fictional wagers in a spreadsheet. If you’re betting with real money, you
should not be betting on every game, only the games you like the most. Whereas
I’m betting on every game, every over/under, because it provides a complete
dataset for macroeconomic analysis. To view my third quarterly report, click here.
My 1st Quarter Profit: $8,927
My 2nd Quarter Profit: $7,206
My 3rd Quarter Profit: $3,714
My 4th Quarter Profit: $2,283
My total profit yield got smaller each quarter, with
just a 2% rate of return in the fourth, which on the bright side was at least a
positive number. The primary driver of my output was favorites on the
moneyline. Betting Arizona to lose was much more profitable in the first half,
before the books over-compensated the line prices in the second half. The
Coyotes finished the regular season atop my Power Rankings, despite Florida
making a run at the top spot in the final weeks. I crushed my early Coyotes
bets before the lines got nerfed.
It was a bad quarter for one of my historically best
categories (until this season), underdogs +1.5 goals. As a whole this
demographic was decent in Q4, but I performed below the market rate. My
performance betting favorites -1.5 goals was not much better. Between faves and
dogs pucklines, I lost more than -$3,000. On moneylines,
I generated more than $5,000 of profit, including at least $1,000 each on
underdogs, favorites, home teams, and visitors. So, while I whiffed on
pucklines, I more than made up the difference by nailing moneylines in nearly
all situations.
Following a busy trade deadline, my expectation was
that the gap between underdogs and favorites would grow wider. For the 30 days
leading up to the March 21st deadline, favorites won 61% of games,
versus 68% in the 30 days following. However, line prices also became more
expensive, as the average implied probability climbed from 63% to 65%. Favorites
on the moneyline might have produced a positive return, but they struggled at
covering the spread -1.5 goals.
Favorites -1.5 goals were a net loser every week
from Jan 17 to Mar 13, but performed very well in the days leading up to the
trade deadline. Given that I was expecting favorites to benefit from all the
new talent they acquired, it seemed like a logical strategy decision to lay
more money down on faves -1.5 goals. Sometimes you see an angle that makes
perfectly reasonable sense, and you’re wrong. For the next 3 weeks after the
deadline, favorites -1.5 goals were losers, while underdogs +1.5 produced
positive returns.
Granted, the subpar performance by favorites on the
puckline -1.5 was not because they were covering less often, but rather the line
prices got too expensive. Home favorites -1.5 goals was among my best
categories in week 23, but I tried to avoid leveraging too much on the
puckline, laying more bets 2 “units” on ML and one “unit” on PL. In week 24, home
favorites covered -1.5 goals in 41% of games, but the lines were so expensive
that they needed to cover 45% to turn a profit.
My hypothesis that pucklines -1.5 goals would boom
in the aftermath of the trade deadline might have proven incorrect for the
first 2-3 weeks, but by week 25 (3 weeks post-deadline), that category really
took off. So in reality, I wasn’t wrong, it just took 3 weeks before I was
right. By that point, I was almost avoiding
pucklines entirely, but at least noted that the category recovered.
The trend heading into final 2 weeks was favorites
booming and underdogs busting, both moneyline and puckline. I had been riding
the Avalanche and Panthers, but otherwise was avoiding extreme favorites in the
-400 range, of which there many. The fourth quarter of the 2021/22 regular
season saw an unprecedented number (in my 3 years tracking lines) of moneyline
favorites in the -400 to -600 range. There were 4 in the entire 2019/20 season,
and 3 in the shortened 2021 season. We didn’t have any from October to the end
of December, but there were 20 in the fourth quarter. It became obscenely
expensive to buy heavy favorites, well above their actual probability of
victory.
I’ve said this many times and it’s worth repeating,
never pay -400 (or worse -500) for a moneyline. I don’t care who the teams are.
It doesn’t matter if the Zamboni driver is the starting goalie, nothing in this
league is a lock. Even a hot Zamboni driver can steal an NHL game. If you bet
$100 on the moneyline for every team favored by at least -400, you lost -$150. They won 75% of
their games, but you needed 82.6% to break even.
While the lines were expensive on the extremes, if
you bet $100 on every moneyline favorite from April 1 to April 23, then you won
$1,364. Favorites -1.5 goals on the puckline performed poorly from March 21 to
April 10, but were outstanding from April 11 to 24, profiting $1,829 if you
laid $100 on each. That output was being driven largely by home teams, and
inspired me to increase my ante on puckline favorites in the final week, which
quickly proved to be another bad decision. After 2 losing bets where Arizona beat
Minnesota and Dallas as an extreme longshot, I immediately reversed course and
started throwing down on the dogs.
One noteworthy trend was big swings between the
winning percentage of home and road teams. Visitors won 33% in week 22, then
61% in week 23, and 38% in week 25. Though I’ve learned from past experience
not to let swings in home/road splits sway my decisions too much, as sometimes
all it takes is a few teams with successful or easy road trips to create the
illusion of a league-wide trend.
Back-to-Backs
Examining the factors limiting my fourth quarter
success, back-to-back games seemed to be a re-occurring theme. Betting against
unrested teams was very profitable in the first half, so the longer the season
went on, the more aggressive my BtB bets became. My assumption was that Covid
could possibly reduce future athletic output in some of the people who catch
it, even athletes. But it also seemed like the sportsbooks were aware, and
shifted lines further and further away from teams with a rest deficit. I often
repeated in my game-by-game notes “this line makes no sense if it weren’t a
back-to-back”.
By late March, I began to notice my strategy was
beginning to backfire; starting with the Columbus Blue Jackets, who failed to
beat a tired Jets team, then covered the puckline the next night vs a rested Minnesota team.
Their failure to comply with the “Law of Back-to-Backs” cost me nearly -$1,000 combined. The very
next week, a tired Islanders team lost to the rested Rangers, then the Rangers
failed to beat a tired Philly team with Martin Jones starting in goal.
There was growing evidence that the “Law of
Back-to-Backs” was failing, but my foot stayed on the gas pedal. Then
catastrophe struck in week 24. Rested teams (a majority on home ice) facing opponents on the 2nd half
of back-to-back games went a combined 5 for 14, costing me -$1,234 from Monday to
Sunday. This might have dampened my enthusiasm, but didn’t convince me to
change strategies, which was the correct decision. For the final 3 weeks of the
season, those rested teams won 71% of their games and helped me win $2,600, erasing the deficit of the preceding weeks.
Live betting
For the Final Quarter of the NHL season, I decided
to try some luck with live betting, having noticed strong value when good teams
were trailing bad teams in the 2nd or 3rd period of
games. These were kept in a separate worksheet and not combined with my overall
results. It was more of a curiosity to see how well I could do. All my bets are
logged the day before games, so I would rarely pay attention to the live
betting lines, except on the weekend when there tends to be teams in action
when I’m logging the next day’s wagers.
My first two live-betting attempts were a success.
Montreal and Tampa were tied 4-4 in the 3rd period with the Lightning being on
a back-to-back, and the Habs were still +200 on the moneyline. They won the
game. Toronto took a 3-2 lead on Philly with 10 minutes left in the 3rd period
and the Leafs were still +105 on the puckline -1.5 goals. They won 6-3. However,
after that initial beginner’s luck, 15 of my next 16 were losers.
Part of the reason for my low rate of success is
that they were almost all in the 2nd or 3rd period trailing
by 1-3 goals. Most of my hedges were in the +500 to +1200 range, so these were
very low probability events. Though hedges only accounted for maybe 60% of my
live wagers, as I was also recording lines of interest when good teams trailed
inferior opponents, but it seemed only Florida was producing positive results.
To really calculate the probability of any given
team erasing a deficit at any time in the game, I would need a data set that
logs the score at the end of every period for a large sample, preferably every
single game. If anyone reading this knows of any such site, please let me know
(the first place I checked was Natural Stat Trick). By the end of the season, I
recorded 44 live bets, at a price of $1,040 and lost -$313. At some point in
the summer I’ll do a more detailed analysis, but my first volley did not reach
the target.
For the live hedges, I was looking for
opportunities where I could wager a small portion of my projected winnings (up
to 1/3) that would also cover the cost of the initial wager and guarantee a
profit with either outcome. Those did not come along very often, and tended to
be later in the 3rd period. It was rarely necessary. Of all my attempted hedges,
only one hit. I had Ottawa +1.5 goals, and put money on the Toronto moneyline
down 2 goals in the 2nd period. Toronto came back and won in
overtime, with both bets being winners. I felt super smart at that moment, but
stepping back and seeing the big picture curbed my excitement.
My 4th Quarter Results:
*Market Bets calculated by betting
exactly $100 on every outcome*
*Whichever teams gets listed -1.5 goals is referred to
by me as the “favorite”, even if the moneylines are the same. When you see
“Washington -1.5 goals” that only refers to those games where they were
“favored” *
Over/Under
The summary of
my 2021/22 regular season over/under results are; outstanding third quarter,
decent every other quarter (noting in Q1 I mostly did not use any algorithms
while accumulating more data). Ironically the first quarter was my second best,
thanks to that run of hot unders, which feels like forever ago already.
I might have been expecting favorites to carry me
in the fourth quarter, but the real driver behind my strong returns was overs.
There were 3-4 weeks in late February, early March were overs either accounted
for a majority of my profit, or were the only thing keeping me above zero. In
week 20, I won $2,517 betting overs and lost -$840 betting everything else. Throughout
the boom, the O/U lines being offered by Draft Kings did not seem to be
adequately compensating for the scoring escalation.
Heading into All-Star weekend, my performance
betting over/under was unsatisfactory, so I took the opportunity to run some
numbers on potential algorithm improvements. Average goals per game for each
team’s last 5 games offered an upgrade based on the existing data. I decided to
continue tracking the recommendations of the first iteration, but relied on the
newer (yet simpler) version. I emerged from the schedule stoppage guns blazing.
For the first month, it was generating a remarkable 15% rate of return, on a
sample size of 237 games.
It was performing so well that I had to run
diagnostic checks to make sure there wasn’t some error in the formula chain. The algorithm's success doesn’t imply that I’m a genius, having simply tried a basic formula which when
applied to the first half data, produced a better result than what I’d been
using. That was the end of my investigation. Simple worked. Perhaps the only
reason I struck gold was an uncommon increase in goal scoring. It could have
just been the perfect formula at the perfect moment and was otherwise not replicable
because it was built on the back of an abnormality.
Another possible explanation for the success was
recording my wagers 30 hours before puck drop when the starting goalies often
aren’t confirmed. The totals being offered can move up and down as more money
is wagered, and especially after the goalies are announced. In multiple weekly
reports I expressed confusion at the Draft Kings lines, but perhaps it was
simply advantageous to bet the opening lines before the market reacts. It’s
also plausible that the bookies weren’t increasing the totals adequately
because prior seasons saw decreases in scoring in the fourth quarter; that
their models ran counter to what was actually happening.
The algorithm
started slowing down at almost exactly the proverbial “quarter pole” (which in
horse racing is a pole that marks the beginning of the final quarter of a
race). By week 22, the books had caught up, and my hot streak cooled off. It
also exposed that if there a trend reversal of any significance (like a
temporary decrease in scoring), the algorithm would struggle both that week and
the following week. The algorithm with the longer memory was better adapted to survive
short-term trend shifts, but also slower to react to prolonged booms and busts.
One was better in my bad weeks, but not nearly as good in my best weeks.
It’s important
not to overreact to good or bad weeks, and stay focused on the grand total of
every bet the algorithm recommended in its lifespan. The last 5 weeks of the
season were a roller-coaster ride with ups and downs, but all in, the peaks
were far greater than the valleys. When all was said and done, the newest
version of my algorithm placed 601 wagers and produced $6,374 in profit for a
6.7% rate of return.
The bonanza on
overs was fantastic, but the algorithm did struggle mightily when promoting
unders. Perhaps during the summer I’ll have to time to dig deeper into why this
happened, and workshop a few possible solutions. Was it based on goalies? It
could have been the algorithm always expects Shesterkin and was a big loser
when Georgiev started. Although looking at the Rangers, I was a net winner on Georgiev
unders this season. Where my algorithm screwed up was recommending the over in
too many Shesterkin starts. Maybe next season I’ll start tracking closing
over/under lines, to approximate how much the starting goalie affected the
market.
Market Best Bets +1.5 Goals: Market Worst
Bets +1.5 Goals:
1) Buffalo Sabres, (+$453) 1) Seattle
Kraken, (-$527)
2) Minnesota Wild, (+$270) 2) Arizona
Coyotes, (-$421)
3) Ottawa Senators, (+$170) 3) Chicago
Blackhawks, (-$417)
Market Best Bets -1.5 Goals: Market Worst
Bets -1.5 Goals:
1) LA Kings, (+$585) 1) Dallas
Stars, (-$770)
2) Vancouver Canucks, (+$465) 2) Florida
Panthers, (-$465)
3) Toronto Maple Leafs, (+$397) 3) Nashville
Predators, (-$415)
My 5 Best 4th quarter Over/Under Bets: Market’s 5
Best 4th quarter Over/Under Bets
1) St. Louis over, (+$1,716) 1) St.
Louis over, (+$998)
2) Arizona over, (+$1,229) 2) Arizona
over, (+$790)
3) Nashville over, (+$1,043) 3) Buffalo
over, (+$671)
4) Toronto over, (+$907) 4)
Washington over, (+$666)
5) Washington over, (+$701) 5) New
Jersey over, (+$533)
My 5 Worst 4th quarter Over/Under Bets:
1) Boston over, (-$926)
2) LA over, (-$850)
3) New Jersey under, (-$800)
4) Pittsburgh over, (-$740)
5) Winnipeg over, (-$659)
My 5 Best Q4 Teams To Bet
On: Market’s 5
Best Q4 Teams To Bet On:
1) Tampa Bay Lightning, (+$2,107) 1) Minnesota
Wild, (+$1,385)
2) Florida Panthers, (+$1,233) 2) Buffalo Sabres, (+$1,194)
3) Buffalo Sabres, (+$948) 3) Toronto
Maple Leafs, (+$986)
4) Colorado Avalanche, (+$756) 4) Ottawa
Senators, (+$639)
5) Boston Bruins, (+$644) 5) Edmonton Oilers, (+$626)
My 5 Worst 4th quarter Teams To
Bet On:
1) New York Rangers, (-$1,181)
2) Carolina Hurricanes, (-$1,000)
3) Montreal Canadiens, (-$559)
4) Edmonton Oilers, (-$509)
5) Chicago Blackhawks, (-$440)
My 5 Best Q4 Teams To Bet
Against: Market’s 5
Best Q4 Teams To Bet Against:
1) Chicago Blackhawks, (+$1,533) 1) Chicago
Blackhawks, (+$1,004)
2) Montreal Canadiens, (+$1,101) 2) Dallas
Stars, (+$883)
3) New Jersey Devils, (+$1,008) 3) Philadelphia
Flyers, (+$782)
4) Pittsburgh Penguins, (+$991) 4) New
Jersey Devils, (+$625)
5) Winnipeg Jets, (+$787) 5) San
Jose Sharks, (+$508)
My 5 Worst 4th quarter Teams To
Bet Against:
1) Minnesota Wild, (-$1,123)
2) Toronto Maple Leafs, (-$1,054)
3) Buffalo Sabres, (-$891)
4) Edmonton Oilers, (-$788)
5) New York Rangers, (-$711)
Team By Team Power
Rankings
The team-by-team
gambling power rankings are ordered by the sum of all my bets on each team to
win or lose for the entire season. They are my own personal power rankings,
reflecting my own success picking the outcome of their games. These aren’t
necessarily the best teams to bet on, as some were swung by a few instances of
good luck or bad judgement. You’ll have to read the team summaries for a deeper
understanding of the replicability. If you are going to be betting on hockey in
the near future, it may help you to read about my own personal success and
failure over the month. For an unbiased look, I will include an overall rank of
account balances if you bet each team to win or lose every game and every
puckline, providing monolithic results of betting both sides consistently team
by team.
LR = League Rank
1) Arizona
Coyotes, ($7,122):
Last Quarter Rank: 1
1st Quarter Profit: $6,333
2nd Quarter Profit: $2,526
3rd Quarter Profit: -$3,194
4th
Quarter Profit: $1,456
Q4 Win-Loss Record: 7-17
Q4 % Money Bet On: 22%
(-$131)
If you bet on them
every game ML+PL: -$641
(LR: 23)
Q4 % Money Bet Against: 78%
($459)
If you bet against
them every game ML+PL: $348 (LR: 10)
Q4 % Bet Over: 95% ($1,229),
Market Return on $1: $1.33
Q4 % Bet Under: 5% (-$100),
Market Return on $1: $0.62
The heating up of the
Arizona Coyotes (on the shoulders of some Keller-Schmaltz magic) cost me dearly
in the third quarter, but that hotness came to a crashing halt when Keller was
injured. They became profitable to bet against once again, though the sportsbooks
charged expensive prices to buy that bet. The reason for my impressive output
on Arizona games was my algorithm crushing their overs, as the team averaged
4.3 goals against per game (up from 3.4, 3.7, 3.7 in the previous quarters). Surely
the departure of Scott Wedgewood at the trade deadline had something to do with
that increase in goals allowed.
Karel Vejmelka
started 17 of their 24 Q4 games, posting an .885 SV% (down from .915 in Q2 and
.894 in Q3), helping juice their overs. The Coyotes futility helped them climb
to the top of my power rankings, and Vejmelka finished #1 in my goalie power
rankings, with nearly a $700 lead on Thatcher Demko. Bad as they were in the
fourth quarter, they actually went 3-0 in the final week of the season as +425,
+360, and +230 underdogs. 2023 will be the Connor Bedard draft, so don’t be
surprised to see Arizona tank even harder next season.
2) Florida
Panthers, ($6,365):
Last Quarter Rank: 3
1st Quarter Profit: $1,437
2nd Quarter Profit: $2,146
3rd Quarter Profit: $1,494
4th
Quarter Profit: $1,288
Q4 Win-Loss Record: 18-5
Q4 % Money Bet On: 86%
($1,244)
If you bet on them
every game ML+PL: -$342
(LR: 19)
Q4 % Money Bet Against: 14%
($148)
If you bet against
them every game ML+PL: -$815 (LR:
26)
Q4 % Bet Over: 64% (-$24),
Market Return on $1: $1.00
Q4 % Bet Under: 36% (-$79),
Market Return on $1: $0.92
The Florida Panthers
won the President’s Trophy and were second to Minnesota for total wins in the 4th quarter; but if you bet them to win every game, your profits were relatively
small considering their dominance. The problem was line price, with a heavy
luxury tax on Panther Ws and an average moneyline of -210. That requires
winning 68% to break even. They won 78% and if you bet Panthers moneyline for
each of them, you only finished with $223 of profit (averaging $40 per win and -$100 per loss). If you
bet every Florida puckline, you lost -$565.
They were 2nd to Tampa for my most profitable team to bet on (ML+PL), with most of that
coming from the moneyline. I found myself leaning on the “2-units moneyline,
1-unit puckline” strategy, but was a net loser on their Q4 pucklines. They
benched half their team for the last 2 games, and I hit a nice pay day taking
Canadiens moneyline +260 to beat the Panthers in the last game of the season,
making me a net winner when betting Florida to lose. Their overs went 6-2-2 in
their first 10 fourth quarter games, then went 2-6-2 for their next 10. This
shift did disrupt my bottom line, as my algorithm generated a small loss on
both overs and unders.
3) Vancouver
Canucks, ($5,436):
Last Quarter Rank: 5
1st Quarter Profit: $40
2nd Quarter Profit: $2,317
3rd Quarter Profit: $1,795
4th
Quarter Profit: $1,284
Q4 Win-Loss Record: 11-11
Q4 % Money Bet On: 59%
($604)
If you bet on them
every game ML+PL: $429 (LR: 7)
Q4 % Money Bet Against: 41%
($238)
If you bet against
them every game ML+PL: -$560 (LR:
24)
Q4 % Bet Over: 80% ($195),
Market Return on $1: $1.03
Q4 % Bet Under: 20% ($248),
Market Return on $1: $0.87
As a Vancouver
resident, I watch more Canucks hockey than any other team by far. They briefly climbed
into the #2 spot in my power rankings with less than 1 week to go in the
regular season, thanks to my efficiency at picking the winner of their games,
running a nice profit on both sides. The team went 11-11, as Elias Pettersson
caught fire and helped keep them alive in the wildcard race longer than they
perhaps deserved (assisted by Dallas slumping). A majority of my wagers
were on Vancouver to win, but also pulled a nice return betting them to lose
back-to-backs.
Considering they were
a .500 team; it was strange that betting them to win had a significantly higher
return. A lot of that had to do with pucklines, which they covered at a high
rate and their opponents did not. They were actually among the better teams in
the league to bet -1.5 goals when favored. Most of my bets against Vancouver came
when they were big favorites against bad teams and I was turned off by the line
price. All my Canucks Q4 profit came in Thatcher Demko starts. I was a net
loser in the 7 games he didn’t start.
4) Tampa Bay
Lightning, ($4,754):
Last Quarter Rank: 17
1st Quarter Profit: $1,400
2nd Quarter Profit: $536
3rd Quarter Profit: -$526
4th
Quarter Profit: $3,343
Q4 Win-Loss Record: 13-10
Q4 % Money Bet On: 78%
($2,107)
If you bet on them
every game ML+PL: -$221
(LR: 16)
Q4 % Money Bet Against: 22%
($721)
If you bet against
them every game ML+PL: $41 (LR: 13)
Q4 % Bet Over: 54% ($399),
Market Return on $1: $1.09
Q4 % Bet Under: 46% ($116),
Market Return on $1: $0.82
The Tampa Bay
Lightning were my best team to bet in the fourth quarter of the season, and by
a substantial margin. I bet the correct outcome in 75% of their games from
March 14 to April 29. This came as somewhat of a surprise, despite all my
weekly reports, this outstanding performance was mostly under my radar until
compiling Q4 stats at the end of the season. Perhaps I should adjust my
spreadsheet to keep a running score of quarterly output, or at least pay more
attention to which teams have climbed the highest in my power rankings week to
week.
It was a below
average quarter from superstar goaltender Andrei Vasilevskiy, who posted a
pedestrian .912 SV%. It didn’t hurt my results at all when Brian Elliot
started, as the back-up was surprisingly good, winning 5 of 6 starts with a
.913 SV% (though I did lose -$126 on Elliot unders). Their Q4 did
include a run of 6 losses in 8 games, which I profited from because their line
prices were too expensive. I lucked into some big wins by defaulting to the
underdog when the lines were off. That’s how I excelled both when betting them
to win and lose.
5) Columbus
Blue Jackets, ($4,745):
Last Quarter Rank: 2
1st Quarter Profit: $1,483
2nd Quarter Profit: $1,414
3rd Quarter Profit: $2,360
4th
Quarter Profit: -$511
Q4 Win-Loss Record: 7-15
Q4 % Money Bet On: 33%
(-$217)
If you bet on them
every game ML+PL: -$699
(LR: 24)
Q4 % Money Bet Against: 67%
(-$473)
If you bet against them
every game ML+PL: -$212
(LR: 16)
Q4 % Bet Over: 83% ($201),
Market Return on $1: $0.97
Q4 % Bet Under: 17% (-$21),
Market Return on $1: $0.95
The BJs were my Cinderella
team for the first 3 quarters, but the clock struck midnight in Q4
(specifically after their April 2nd game vs Boston). Their overs
were on a 6-2 run, immediately followed by their unders going on a 6-2-1 run. They
were the only other team to hold the #1 spot in my Power Rankings, however
briefly, in weeks 1 and 21. They only won 32% of their Q4 games and 67% of my
money was on their opponents, producing a -$473 loss. They were losing a lot, just not
when I went heavy on the opposition.
There were 3 games
specifically that cost me -$1,550 betting their opponents; when the BJs
won or covered on the second half of back-to-back sets against Minnesota and
Philadelphia, then another where they upset Edmonton without Laine or Werenski.
At least 2 of those could be considered “bad beats”. Despite that low winning
percentage, they generated a small profit as underdogs +1.5 goals, which also
means they were bad to bet against -1.5 goals (which I didn’t take often). For
the first three quarters, the sportsbooks were incorrectly devaluing their
lines, but eventually this team became who the books thought they were.
6) Chicago
Blackhawks, ($4,326):
Last Quarter Rank: 6
1st Quarter Profit: $1,632
2nd Quarter Profit: $84
3rd Quarter Profit: $2,065
4th
Quarter Profit: $545
Q4 Win-Loss Record: 6-16
Q4 % Money Bet On: 33%
(-$440)
If you bet on them
every game ML+PL: -$1,563
(LR: 32)
Q4 % Money Bet Against: 67%
($1,533)
If you bet against
them every game ML+PL: $1,004 (LR: 1)
Q4 % Bet Over: 82% (-$319),
Market Return on $1: $1.08
Q4 % Bet Under: 18% (-$230),
Market Return on $1: $0.82
The Chicago
Blackhawks had a terrible fourth quarter, winning only 6 of 22 games, and I performed
very well when betting them to lose. My mistakes came picking them to win, but
that was mostly in the final dozen games when they played a very easy schedule.
So, I only invested in Chicago wins when they faced bad teams, or occasionally
against a good opponent who played the night before. The Hawks blew a few
opportunities against tired opposition that cost me large wagers. They were
equally bad at home as they were on the road, as venue did not play a
significant factor in my results.
The bigger roadblock
stopping me from maximizing my returns was over/under. Their overs went 12-9-1
and 82% of my money was on that outcome, yet I lost -$319 on that wager. It
surprised me to learn that I lost -$400 betting overs in Collin Delia starts
(specifically in 2 games against Florida and Los Angeles). He became the
back-up when Fleury was shipped out, and only started 5 games with a .908 SV%.
It wasn’t that he was awesome in those 2 games, but they went under because the
team couldn’t score.
7) New York
Islanders, ($4,266):
Last Quarter Rank: 4
1st Quarter Profit: $1,513
2nd Quarter Profit: $1,918
3rd Quarter Profit: $970
4th
Quarter Profit: -$135
Q4 Win-Loss Record: 13-13
Q4 % Money Bet On: 45%
($130)
If you bet on them
every game ML+PL: -$82
(LR: 12)
Q4 % Money Bet Against: 55%
(-$559)
If you bet against
them every game ML+PL: -$356 (LR:
21)
Q4 % Bet Over: 89% ($412),
Market Return on $1: $1.11
Q4 % Bet Under: 11% (-$118),
Market Return on $1: $0.81
This was my worst
quarter betting the Islanders and it could have been much worse had their overs
not gone 14-10-2. For most of the season I was effective at picking moneyline
winners of NYI games, but my gift of foresight became clouded in Q4, laying too
much money on the opposition. They won 50% of their games, with my money 45% on
the Islanders and 55% on the opponents. My anti-NYI bets were a big loser,
although -$1,000 of that came from 2
games where the Isles were on short rest and still won anyway. Delete those
from the sample, and it was a good quarter.
For the second
consecutive quarter, they were a strong “over” team, averaging 6.1 total goals
per game, after being down around 5 for most of the first half. Overs were my
best Isles category, and that’s despite losing -$367 on overs in Ilya Sorokin starts, vs
+$780 when Sorokin was sitting on the bench. This is one example where it
really hurt me to make my picks before the starter was named, but the Islanders
were among the most secretive teams when it came to making that information
public. You would often need to wait until close to puck drop before logging
your bet, which wasn’t feasible for me given the parameters of my little
experiment. I have a job and can’t wait at the computer all day waiting to see
who leads the team out for warm-ups.
8) Minnesota
Wild, ($3,152):
Last Quarter Rank: 8
1st Quarter Profit: $1,658
2nd Quarter Profit: $253
3rd Quarter Profit: $1,640
4th
Quarter Profit: -$400
Q4 Win-Loss Record: 19-5
Q4 % Money Bet On: 73%
($375)
If you bet on them
every game ML+PL: $1,385 (LR: 1)
Q4 % Money Bet Against: 27%
(-$1,123)
If you bet against
them every game ML+PL: -$2,018 (LR:
32)
Q4 % Bet Over: 78% (-$21),
Market Return on $1: $0.88
Q4 % Bet Under: 22% ($369),
Market Return on $1: $1.04
The Wild won more
games than any other team in the fourth quarter of the NHL season, and despite 73%
of my money being invested in that outcome, I only produced a small return on
Wild wins. Meanwhile, that 27% investment in their opponents was a big loser,
but -$750 of that came in 2
games were the Wild (on short rest) beat Colorado and Washington, which were
defensible decisions on my part. On the other side, I would have generated more
than $1,000 profit betting them to win had it not been for 2 games, a home loss
to Pittsburgh and a failure to cover a puckline against a tired Columbus team
when Merzlikins played the night before, then played the next night. I was
expecting the BJ back-up.
The team’s big
acquisition at the trade deadline was goaltender Marc-Andre Fleury, but that
actually did very little to improve the team, unless that’s what lit a fire
under Cam Talbot, who posted a .926 SV% in Q4 (up from .899 in Q3, which was
probably what convinced management that goaltending help was needed). That
massive improvement in quality of goaltending explains why they went from the 5th best over team in Q3 to the 26th in Q4. My algorithm recommended too
many overs, but still performed very well considering how far their trend
shifted.
9) Buffalo
Sabres, ($2,621):
Last Quarter Rank: 10
1st Quarter Profit: $575
2nd Quarter Profit: -$976
3rd Quarter Profit: $2,872
4th
Quarter Profit: $149
Q4 Win-Loss Record: 12-10
Q4 % Money Bet On: 37%
($948)
If you bet on them
every game ML+PL: $1,194 (LR: 2)
Q4 % Money Bet Against: 63%
(-$891)
If you bet against
them every game ML+PL: -$1,595 (LR:
31)
Q4 % Bet Over: 73% ($550),
Market Return on $1: $1.30
Q4 % Bet Under: 27% (-$457),
Market Return on $1: $0.60
It might shock you to
learn that the Buffalo Sabres had a winning record in the fourth quarter of the
season, something that I did tap into, but not nearly as much as I could/should
have. They were actually the #2 team in the entire league to bet to win, as
well as the #3 team to bet over. If you bet a $100 parlay on Sabres to win with
the over in all Buffalo Q4 games, you would have banked nearly $2,000. The only
other team that might come close to that is St. Louis. Exploring over/under
parlay combinations is 100% on my summer to-do list.
My Q4 Buffalo results
would have been much better had I jumped on the bandwagon sooner. I was late to
the party and only walked away with a small profit. There were 2 upset victories
against Calgary and Carolina that accounted for a majority of my poor
performance betting them to lose. My algorithm was only 2 for 7 when
recommending Buffalo unders, which is interesting because my older algorithm
that looked at 10-game samples disagreed with all those under selections. The
newer version overreacted to a 1-0 victory against Calgary and lost -$300 incorrectly
recommending unders in the next 5 games.
10) Anaheim Mighty
Ducks, ($2,525):
Last Quarter Rank: 12
1st Quarter Profit: $1,167
2nd Quarter Profit: $212
3rd Quarter Profit: $748
4th
Quarter Profit: $398
Q4 Win-Loss Record: 4-16
Q4 % Money Bet On: 24%
(-$181)
If you bet on them
every game ML+PL: -$1,295
(LR: 30)
Q4 % Money Bet Against: 76%
($785)
If you bet against
them every game ML+PL: $443 (LR: 8)
Q4 % Bet Over: 87% ($194),
Market Return on $1: $1.11
Q4 % Bet Under: 13% (-$400),
Market Return on $1: $0.80
The Anaheim Ducks won only 4 times in 20 fourth quarter games, and I performed well when betting
them lose, which became a more expensive proposition the further they spiraled
down the toilet bowl. They may have started the season strong, but much of that
had to do with John Gibson’s .920 first half SV%. Team scoring might have only
declined slightly in the 2nd half, but goal allowing increased
substantially. The Ducks were not very mighty at home, where they went 1-9, and
where I had my best success betting their opponents. The returns betting them
to lose were relatively small despite losing 80% of their matches because of
line price, with their opponents averaging roughly -200 on the moneyline.
Much like Q3, the
Ducks continued to be a strong over team (with overs going 10-7-3) as John
Gibson was just a shadow of his first half self. My algorithm did underperform
when Duck hunting, going 0-3 on its under recommendations (which if you do the
math, means overs went 7-7-3 when I bet over). The irony is, Anthony Stolarz
was their better goalie (.913 SV% to Gibson’s .898), but it was Stolarz who
blew my under bets. Keep in mind, my algorithm doesn’t care which goalie
starts.
11) Vegas
Golden Knights, ($2,404):
Last Quarter Rank: 11
1st Quarter Profit: -$353
2nd Quarter Profit: $1,100
3rd Quarter Profit: $1,689
4th
Quarter Profit: -$32
Q4 Win-Loss Record: 11-10
Q4 % Money Bet On: 51%
($231)
If you bet on them
every game ML+PL: -$530
(LR: 20)
Q4 % Money Bet Against: 49%
(-$163)
If you bet against
them every game ML+PL: $396 (LR: 9)
Q4 % Bet Over: 59% ($199),
Market Return on $1: $1.19
Q4 % Bet Under: 41% (-$299),
Market Return on $1: $0.75
The Vegas Golden
Knights stunned the hockey world by falling short of a playoff spot, despite
being a top contender to win the Stanley Cup. It was their 8-12 record in Q3
that did most of the damage, when they were my #2 team to bet against. The
problem for me was that they improved in Q4, but I began picking them to lose
more often, leading to a monetary loss. Starting goaltender Robin Lehner
battled injury down the stretch, posting an .892 SV% in 6 starts. Whereas Logan
Thompson was actually good in his place, going 9-6 with a .915 SV%.
My over/under
algorithm struggled with Vegas when Lehner was in goal (-$326) and did well on
overs for the other gatekeepers. Vegas overs went 12-7-2 after going 7-12-1 in
Q3. That shift was partially correlated to the deterioration of Lehner, but
also the Knights climbed from 2.4 goals per game in Q3 up to 3.5 in Q4. Obviously,
they didn’t score enough goals to make the playoffs, but the offence did start
rolling down the stretch. One big reason they missed the playoffs was only
winning 40% of their road games in the 2nd half.
12) Philadelphia
Flyers, ($2,197):
Last Quarter Rank: 15
1st Quarter Profit: $595
2nd Quarter Profit: $378
3rd Quarter Profit: $805
4th
Quarter Profit: $418
Q4 Win-Loss Record: 7-16
Q4 % Money Bet On: 27%
(-$327)
If you bet on them
every game ML+PL: -$1,238
(LR: 29)
Q4 % Money Bet Against: 73%
($280)
If you bet against
them every game ML+PL: $782 (LR: 3)
Q4 % Bet Over: 82% ($590),
Market Return on $1: $1.03
Q4 % Bet Under: 18% (-$125),
Market Return on $1: $0.87
This was a terrible
season for the Philadelphia Flyers, but they did improve in the fourth quarter,
affecting my bottom line. They won 30% of their games, up from 26% in Q3 and
23% in Q2. They improved, but still I performed poorly when betting them to win.
I was correct to have more confidence in Philly to win, but chose the wrong
games to bet on it. My yield on their losses under-performed their market rate;
as I was +$878 betting against Hart-Sandstrom, and lost -$599 betting against
Martin Jones. Yeah, Jones and his .901 Q4 SV% was stealing money from me,
although most of that came from one road game on short rest against the
Rangers.
The bigger windfall
was Philly overs, as Q4 was their biggest for both goals scored and allowed.
That’s where Martin Jones paid me back, as I was +$588 on his overs. My issue
with Jones was his goal support, not his outstanding quality of play (the
Flyers averaged 3.2 goals per game when MJ started, and 2.2 when he was the
back-up). Carter Hart recorded an awful .870 SV% in Q4, and my results would
have theoretically improved if he had started more than 7 games.
13) New
Jersey Devils, ($1,956):
Last Quarter Rank: 16
1st Quarter Profit: $877
2nd Quarter Profit: -$125
3rd Quarter Profit: $856
4th
Quarter Profit: $348
Q4 Win-Loss Record: 5-18
Q4 % Money Bet On: 16%
($110)
If you bet on them
every game ML+PL: -$1,375
(LR: 31)
Q4 % Money Bet Against: 84%
($1,008)
If you bet against
them every game ML+PL: $625 (LR: 4)
Q4 % Bet Over: 67% ($30),
Market Return on $1: $1.23
Q4 % Bet Under: 33% (-$800),
Market Return on $1: $0.67
New Jersey was dealt
a devastating blow with Jack Hughes season ending injury, which should have
hurt their overs, but didn’t (going 7-5 for their last 12 games). Goal scoring
did decrease, but goal allowing offset that change. The Devils went 5-18 in Q4
and were my 3rd best team to bet against overall. Although it’s
important to point out that my $1,008 profit betting them to lose would have
been $508 had the Devils not blown a 6-2 third period lead to the Florida
Panthers (in the game that inspired me to start tracking live betting). They
might have been a good team to bet against, but only on the moneyline. They
returned a positive number +1.5 goals.
What really hurt my
performance was my algorithm’s inability to pick the right unders. Their overs
went 15-8 with my algo going 1-8 when recommending unders. Problem was, they
had back-to-back low scoring games in mid-March, then my algorithm blew -$500 on unders in their
next 3 games. Once again, the algorithm which looked back 10 games disagreed
with those bets. My original formula was better at picking NJD outcomes in the
fourth quarter. Their best 2 goalies were injured for most of Q4, with Andrew
Hammond and Nico Daws starting 19 of 23. Daws was decent at first, then slipped
after the Hughes injury.
14) Washington
Capitals, ($1,697):
Last Quarter Rank: 14
1st Quarter Profit: $2,294
2nd Quarter Profit: -$661
3rd Quarter Profit: $327
4th
Quarter Profit: -$264
Q4 Win-Loss Record: 12-10
Q4 % Money Bet On: 60%
(-$339)
If you bet on them
every game ML+PL: -$233
(LR: 17)
Q4 % Money Bet Against: 40%
(-$326)
If you bet against
them every game ML+PL: -$473 (LR:
22)
Q4 % Bet Over: 92% ($701),
Market Return on $1: $1.30
Q4 % Bet Under: 8% (-$300),
Market Return on $1: $0.63
The Washington
Capitals went 12-10 to wrap up the schedule and I posted a loss both when
betting then to win and lose; though had it not been for a road win vs Carolina
and a home loss to Dallas, I would have posted a positive number on both sides.
They became a much less reliable team to bet in the second half, and were
especially bad -1.5 goals as favorites, while posting a gain as underdogs +1.5.
They were at least playing in a lot of close games against better teams. For
the third consecutive quarter, they had a better record on the road than at
home. Coincidently, I lost -$748 betting on their home games, and was
+$82 on their road games.
The Caps were able to
hold the #14 slot despite losing money thanks to my algorithm crushing their
overs. There was inconsistency in goal, as Vanecek and Samsonov took turns as
the primary starter. Fortunately, they were both equally bad (.887 and .879
save percentages), which meant the over was a good bet regardless of who got
nod. Yet both goalies had winning records despite porous performance because
the Washington offense lit the lamp more often.
15) Boston
Bruins, ($1,654):
Last Quarter Rank: 13
1st Quarter Profit: $1,620
2nd Quarter Profit: $49
3rd Quarter Profit: $312
4th
Quarter Profit: -$328
Q4 Win-Loss Record: 15-8
Q4 % Money Bet On: 77%
($644)
If you bet on them
every game ML+PL: -$97
(LR: 13)
Q4 % Money Bet Against: 23%
($43)
If you bet against
them every game ML+PL: -$150 (LR:
15)
Q4 % Bet Over: 68% (-$926),
Market Return on $1: $0.77
Q4 % Bet Under: 32% (-$89),
Market Return on $1: $1.17
The Bruins posted their
best winning percentage of the season in the fourth quarter, and I did quite
well investing in their wins, but struggled with their over/under. One of the
keys to Boston’s success all season was a strong road winning percentage,
which was reflected on my stat sheet as well, as I was +$2,194 betting Boston
on the road, and -$890 at home (not
including over/under) from October to April. 77% of my money was on Boston to
win in the 4th quarter, which netted me $644, and even pulled $43
when betting them to lose. The only reason that I lost money on Boston’s Q4
games was an abysmal performance on their over/unders.
My algorithm
recommended a 68% stake in their overs, despite their unders producing a much
better return. Linus Ullmark posted outstanding Q4 numbers, going 9-1 with a
.945 SV%; whereas Jeremy Swayman went 6-7 with an .887. Ullmark was the thief
who stole a large chunk of my over bets. One of the reasons the algorithm was
recommending overs so often when their unders went 12-7-4 is because they were
involved in handful of low scoring games against high scoring teams; also
having a big disparity between goaltenders was a problem for an algorithm that
doesn’t care who is starting.
16) Carolina
Hurricanes, ($1,521):
Last Quarter Rank: 9
1st Quarter Profit: $1,181
2nd Quarter Profit: -$821
3rd Quarter Profit: $2,484
4th
Quarter Profit: -$1,323
Q4 Win-Loss Record: 13-10
Q4 % Money Bet On: 61%
(-$1,000)
If you bet on them
every game ML+PL: $15 (LR: 10)
Q4 % Money Bet Against: 39%
(-$57)
If you bet against
them every game ML+PL: -$345 (LR:
20)
Q4 % Bet Over: 65% ($260),
Market Return on $1: $1.11
Q4 % Bet Under: 35% (-$527),
Market Return on $1: $0.79
I entered the fourth
quarter flying high on a Hurricanes hot streak, and during that first week was
knee deep in my Q3 Report outlining my remarkable performance betting them to
win. Within a 6-day window (in the middle of which my Q3 Report was published),
I lost -$1,550 on 3 games picking
Carolina to win at home vs the Rangers, Capitals, and Stars (which included a
miraculous Alexandar Georgiev 44-save shutout). After that stretch of games, my
wagers shifted more to Carolina opponents. Though it was not something I had
deliberately planned to do, but rather was a function of disagreeable line
prices. I might have written in my week 21 betting report that my confidence
wasn’t shaken, but there was an impact on the price I was willing to pay for
their wins.
The storm surge was
not so mighty in the fourth quarter, their weakest of the season. They would
eventually lose their two best starting goaltenders to injury. Frederik
Andersen was 5-7 with a .897 SV% while Antti Raanta was 5-3 with a .903.
Despite Raanta’s slightly higher SV%, his overs had a much higher rate of
return than Andersen as the Canes scored 1.3 more goals per game when Raanta
started (and gave up more shots against).
17) New York
Rangers, ($1,493):
Last Quarter Rank: 7
1st Quarter Profit: $901
2nd Quarter Profit: $1,610
3rd Quarter Profit: $1,084
4th
Quarter Profit: -$2,102
Q4 Win-Loss Record: 15-8
Q4 % Money Bet On: 75%
(-$1,181)
If you bet on them
every game ML+PL: $357 (LR: 8)
Q4 % Money Bet Against: 25%
(-$711)
If you bet against
them every game ML+PL: -$343 (LR:
19)
Q4 % Bet Over: 67% (-$490),
Market Return on $1: $0.83
Q4 % Bet Under: 33% ($281),
Market Return on $1: $1.09
The Rangers dropped
significantly in my power rankings during the fourth quarter, thanks to 3
things; 1) betting them to win home moneyline, 2) betting them to lose on the
road, 3) overs. They were simultaneously among my worst teams to bet on or
against, but most of the damage was done investing in their wins, which had
produced strong returns for me in the previous 2 quarters. There were 2 games
where they failed to beat non-playoff teams who were on the second half of a
back-to-back that cost me -$1,260.
I’ll have to do a
diagnostic investigation this summer as to why my algorithm recommended a 67%
stake in Q4 Ranger overs. There was an Igor Shesterkin cold streak in there
somewhere, but that’s no excuse. I presume that they faced a high percentage of
high scoring teams, where I assigned equal weight to their opponent’s goals in
games where Shesterkin was not the goaltender. There was a stretch of 5 games
where 3 of them had at least 9 goals scored, and my algorithm went heavy on
overs in the next 5 games and lost -$443. I was -$743 on their overs when Shesterkin started
and +$252 with Georgiev. Making my bets before the starter was known hurt me here.
18) San Jose
Sharks, ($1,062):
Last Quarter Rank: 19
1st Quarter Profit: -$537
2nd Quarter Profit: $242
3rd Quarter Profit: $1,534
4th
Quarter Profit: -$176
Q4 Win-Loss Record: 6-18
Q4 % Money Bet On: 31%
(-$386)
If you bet on them
every game ML+PL: -$1,074
(LR: 28)
Q4 % Money Bet Against: 69%
($582)
If you bet against
them every game ML+PL: $508 (LR: 5)
Q4 % Bet Over: 74% (-$390),
Market Return on $1: $0.93
Q4 % Bet Under: 26% ($18),
Market Return on $1: $0.99
The Sharks were above
.500 in the first half of the season, then far below in the second half. I laid
too much on them to win/cover, but mostly when there was too much juice on
their opponents. Though my algorithm that uses full-season win-loss records to approximate
probability of victory was misleading after they got worse. It kept telling me
the lines were off, when infact the team was just worse and the betting lines
adjusted faster. Though I still had a 69% stake in Shark opponents, because I
regularly check how teams have fared in their last 10 games. There was a game
when the Sharks won in Calgary that cost me a $500 bet, but otherwise
betting them to lose was a sound investment.
They acquired Kaapo
Kahkonen at the trade deadline, and the young netminder was decent posting a
.916 SV%, but with a 2-7 record. Betting Kahkonen to lose was a winning wager. My
over/under algorithm only recommended a 26% stake in San Jose unders, which was
a slightly better Q4 wager (going 12-11-1). They were quietly one of the better
under teams this season, but in the final quarter, Kahkonen was the only goalie
that delivered a profit if you bet each.
19) Calgary
Flames, ($919):
Last Quarter Rank: 23
1st Quarter Profit: -$1,398
2nd Quarter Profit: $335
3rd Quarter Profit: $1,306
4th
Quarter Profit: $677
Q4 Win-Loss Record: 14-9
Q4 % Money Bet On: 71%
(-$121)
If you bet on them
every game ML+PL: $11 (LR: 11)
Q4 % Money Bet Against: 29%
($442)
If you bet against
them every game ML+PL: -$514 (LR:
23)
Q4 % Bet Over: 78% ($405),
Market Return on $1: $1.08
Q4 % Bet Under: 22% (-$50),
Market Return on $1: $0.83
The Calgary Flames
won 61% of their games in the 4th quarter, and 71% of my money was
invested in that outcome, yet managed to produce a net loss when doing so,
thanks to -$1,000
coming from a pair of blown pucklines against San Jose and Buffalo (on a
back-to-back). Those 2 losing bets came while I was writing my Q3 betting
report where the Flames were the best bet -1.5 goals in the whole league. As
soon as I Tweeted that information, they blew the next two. Although they did
go on to turn a Q4 puckline profit, whereas if you bet $100 on them to win
every moneyline, you lost -$289; with the average line being -210,
which requires winning 68% to break even.
My earnings were
actually higher when betting them to lose, but all that came from one game
where the Avalanche were visiting Calgary and were +100 underdogs, so I threw
down $500 on Colorado. Calgary overs went 12-9-2, and my algorithm generated a
nice return on their games. Jacob Markstrom slipped down the stretch (possibly
from over-use) as his SV% dropped to .905, boosting his overs. I was surprised
to see that a big chunk of my Calgary profit came with Dan Vladar in goal as he
started 4 of their last 6 games.
20) Dallas
Stars, ($653):
Last Quarter Rank: 20
1st Quarter Profit: $597
2nd Quarter Profit: $509
3rd Quarter Profit: -$9
4th
Quarter Profit: -$444
Q4 Win-Loss Record: 14-11
Q4 % Money Bet On: 47%
(-$312)
If you bet on them
every game ML+PL: -$1,050
(LR: 27)
Q4 % Money Bet Against: 53%
(-$472)
If you bet against
them every game ML+PL: $883 (LR: 2)
Q4 % Bet Over: 74% ($236),
Market Return on $1: $0.92
Q4 % Bet Under: 26% ($104),
Market Return on $1: $0.99
The Dallas Stars lost
their best defenseman Miro Heiskanen to mono for a chunk of the 4th quarter,
prompting me to lay more money on their opponents. Turns out, the team
performed remarkably well despite losing such an important player, at least in
the immediate aftermath. There were two games specifically that cost me -$1,150; both on the road
where Dallas has been worse all season, beating Washington despite short-rest,
and then Carolina. They were actually one of the best teams to bet against in
Q3, but didn’t start to struggle until after Heiskanen returned to the line-up.
They went on a run of 4 wins in 11 games in early April, and probably would
have missed the playoffs if Vegas didn’t choke.
My pledge to short
Dallas after losing Heiskanen cost me some big bets, and when they won my
confidence after their star returned, it cost me again. That’s how I was a
loser on both sides. On the bright side, my algorithm did a respectable job on
their over/unders (after losing money in both Q2 & Q3). Or at least it
performed well on Scott Wedgewood overs and Jake Oettinger unders (which is a
little strange given Wedgewood had a higher Q4 SV%). All categories, I was
+$764 in Oettinger’s 18 starts and -$1,208 in just 7 Wedgewood starts.
21) Colorado
Avalanche, ($281):
Last Quarter Rank: 24
1st Quarter Profit: -$486
2nd Quarter Profit: $1,925
3rd Quarter Profit: -$1,535
4th
Quarter Profit: $377
Q4 Win-Loss Record: 14-8
Q4 % Money Bet On: 93%
($756)
If you bet on them
every game ML+PL: -$257
(LR: 18)
Q4 % Money Bet Against: 7%
($130)
If you bet against
them every game ML+PL: -$276 (LR:
18)
Q4 % Bet Over: 59% (-$553),
Market Return on $1: $0.79
Q4 % Bet Under: 41% ($45),
Market Return on $1: $1.14
The Colorado
Avalanche finished as the 2nd best team in the NHL and won 14 of 22
fourth quarter games, but if you bet $100 on their moneyline to win every one,
you actually lost -$15 because their line
prices were so expensive. People (myself included) love laying dough on the Avs
to win, so you’re going to pay a tax to do so. Sometimes you get a reasonable
line when they play elite teams. From January 2021 to May 2022, the Avs moneyline
was greater than zero only 8 times, and I picked Colorado in 7 of those. If I
see an Avs moneyline getting plus money, it’s irresistible to me.
Betting $100 on every
Avs puckline -1.5 goals would have netted you -$326, as they covered an insufficient number
to pay the tax. I got burned big time on Avs pucklines in Q3, so played it safe
in Q4, which was the right call. I would have done even better betting the Avs
to win had they not followed up a 9-game winning streak with a 4-game losing
streak near the end of the season that siphoned off a decent chunk of my
bankroll. My algorithm also struggled with this team, recommending too many
overs when they were a better under team. The problem there being that there a
few blowouts which were followed by unders. Kuemper was the goalie you wanted
in net if you were betting under.
22) Montreal
Canadiens, ($259):
Last Quarter Rank: 22
1st Quarter Profit: $7
2nd Quarter Profit: $1,926
3rd Quarter Profit: -$1,559
4th
Quarter Profit: -$115
Q4 Win-Loss Record: 6-17
Q4 % Money Bet On: 29%
(-$559)
If you bet on them
every game ML+PL: -$608
(LR: 22)
Q4 % Money Bet Against: 71%
($1,101)
If you bet against
them every game ML+PL: $458 (LR: 6)
Q4 % Bet Over: 80% (-$42),
Market Return on $1: $1.17
Q4 % Bet Under: 20% (-$617),
Market Return on $1: $0.75
Normally betting
against the league’s worst teams is one of my super powers, but it surprised me
to see how often I was laying money on a 6-17 team. Part of the problem was
line price, which tended to be obscenely expensive when facing good teams
(where I drifted to the puckline +1.5 goals, and did generate profit from those
wagers). Where I really ran into trouble early in Q4 was betting Montreal to
beat non-playoff teams on back-to-backs (Senators, Jets, and Islanders). Sam
Montembeault was responsible for most of my failed “Montreal to win/cover”
wagers. It would have been even worse had I not hit 2 nice jackpots in the last
week when the Habs beat the Rangers and Panthers as big underdogs.
What really kicked me
in the ass more than anything was over/unders, which was problematic for me all
season. In Q4, it was entirely Jake Allen’s fault. In his 11 starts before
getting injured, I lost -$244 on overs and -$417 on unders. Most of
my money was on the overs, which went 13-8-2, yet somehow I lost -$42 on those bets, then
went 1-5 on unders. Digging deeper into the numbers, the Habs averaged 2.1
goals per game when I bet the over (not counting the last week), versus
averaging 3.1 goals per game in the quarter.
23) Seattle
Kraken, ($184):
Last Quarter Rank: 18
1st Quarter Profit: $1,343
2nd Quarter Profit: -$14
3rd Quarter Profit: $46
4th
Quarter Profit: -$1,191
Q4 Win-Loss Record: 9-12
Q4 % Money Bet On: 32%
(-$383)
If you bet on them
every game ML+PL: -$161
(LR: 15)
Q4 % Money Bet Against: 68%
(-$678)
If you bet against
them every game ML+PL: -$233 (LR: 17)
Q4 % Bet Over: 52% (-$164),
Market Return on $1: $0.87
Q4 % Bet Under: 48% ($33),
Market Return on $1: $1.05
The Seattle Kraken won
43% of their games in the fourth quarter, ranking as their best quarter of the
season. Sadly, the Kraks winning more games led to me losing more bets, as I
had not anticipated this reversal of misfortune. They unloaded significant
talent at the trade deadline, so I had earmarked them for a nosedive. My
biggest loss was a longshot victory against Colorado, which accounted for
nearly half of my total Q4 Kraken monetary loss. That win inspired me to bet
Seattle to win/cover in 5 of their last 6 games, and they only won once in that
span.
I did lay down more
money on Seattle to win than Q2 or Q3, but reviewing my comments on each wager,
the common theme was complaining about the opponent’s line price. In those
situations, my bets tend to be small. It was death by a thousand cuts. Very
seldom did I hit both the ML/PL bet and the over/under. They tended to offset,
one or the other. My over/under algorithm went -$417 on Chris Dreidger overs, -$122 on CD unders, and
went +$408 on all wagers when Grubauer or Daccord started. The team’s unders
actually went 10-8-3, after being a strong over team in previous quarters.
Dreidger rebounded from an awful start of the season, posting a .922 SV% in 9
fourth quarter starts.
24) St. Louis
Blues, ($25):
Last Quarter Rank: 28
1st Quarter Profit: -$824
2nd Quarter Profit: $771
3rd Quarter Profit: -$1,530
4th
Quarter Profit: $1,608
Q4 Win-Loss Record: 15-8
Q4 % Money Bet On: 64%
($187)
If you bet on them
every game ML+PL: $539 (LR: 6)
Q4 % Money Bet Against: 36%
(-$87)
If you bet against
them every game ML+PL: -$915 (LR:
27)
Q4 % Bet Over: 89% ($1,716),
Market Return on $1: $1.43
Q4 % Bet Under: 11% (-$209),
Market Return on $1: $0.50
It’s been a down and
up and down and up kinda season for me betting Blues games. Note to self:
insert trampoline joke. In all four quarters, I lost money when betting the
Blues to lose. But I was also a net loser when betting them to win. They were
an excellent road team down the stretch, going 9-3 in Q4 as St. Louis road
moneyline produced strong returns for my portfolio (+$595); whereas betting
them to win at home led me to a -$459 loss. They were once again profitable
to bet -1.5 goals as favorites, returning a positive number in all 4 quarters
(though I mostly stayed away from that wager because St. Louis and I have trust
issues).
The only reason this
team climbed my power rankings in the fourth quarter was because St. Louis
overs was one of my best bets (#2 behind Lightning to win). My algorithm
recommended an 89% stake in overs, which turned into a winning lottery ticket. The
performance gap between the goalies narrowed, with Husso posting a .906 Q4 SV%,
and Binnington .894. Both goalies generated big returns on overs, although
Binnington had a higher rate per $1 bet. Blues overs went 45-31-6 on the
season.
25) Detroit
Red Wings, ($23):
Last Quarter Rank: 26
1st Quarter Profit: -$1,752
2nd Quarter Profit: -$101
3rd Quarter Profit: $686
4th
Quarter Profit: $1,190
Q4 Win-Loss Record: 8-15
Q4 % Money Bet On: 44%
(-$41)
If you bet on them
every game ML+PL: -$159
(LR: 14)
Q4 % Money Bet Against: 56%
($456)
If you bet against
them every game ML+PL: -$10 (LR:
14)
Q4 % Bet Over: 51% ($624),
Market Return on $1: $1.15
Q4 % Bet Under: 49% ($152),
Market Return on $1: $0.80
As a Red Wings fan
who wants the highest possible draft pick, it brings me pleasure generating
profit from their losses; which became a more lucrative investment in the
second half. They only won 35% of their games, but if you bet every opponent
moneyline and puckline, you actually lost money. They had 13 games where their
opponent was favored by at least -200, and the Wings won often enough that it
produced a negative return. I actually bet Detroit to win or cover in 16 of
their 23 Q4 games, but they were mostly small bets on the Wings as longshots,
which only led to a -$41 loss. Whereas my
bets on Detroit opponents tended to be large wagers when the line was more
fairly priced, leading to a $456 gain.
The goaltending was
porous in Q4, with Nedeljkovic posting a .901 SV% and Greiss posting an .891.
My profit was +$1,676 when Nedeljkovic started, vs -$704 with Greiss. My
algorithm was very efficient at navigating their over/under despite ignoring
goalies, with Detroit overs going 12-8-3, with me turning a profit on both
sides. My algorithm went 9-2-3 predicting over/under when Nedeljkovic started,
and lost money when Greiss started.
26) Edmonton
Oilers, (-$95):
Last Quarter Rank: 21
1st Quarter Profit: $1,572
2nd Quarter Profit: -$84
3rd Quarter Profit: -$950
4th
Quarter Profit: -$633
Q4 Win-Loss Record: 17-6
Q4 % Money Bet On: 60%
(-$509)
If you bet on them
every game ML+PL: $626 (LR: 5)
Q4 % Money Bet Against: 40%
(-$788)
If you bet against
them every game ML+PL: -$1,496 (LR:
30)
Q4 % Bet Over: 44% ($686),
Market Return on $1: $1.13
Q4 % Bet Under: 56% (-$21),
Market Return on $1: $0.81
The Oilers won 74% of
their games in the 4th quarter, yet I was unable to turn a profit on
their victories, thanks largely to a missed $500 bet when they failed to beat a
tired Columbus team on a back-to-back. Edmonton was a tale of two goaltenders
in Q4, with nearly a 50-50 start split; Mike Smith was 11-2 with a .941 SV% and Mikko
Koskinen was 6-4 with an .893. Making my picks before the starter was known
certainly hurt me with Edmonton, as I was -$753 betting Koskinen to win, and -$875 betting Mike Smith
to lose. It’s worth noting that Koskinen posted a .922 SV% in Q3, while Smith
was at .885; so the hot streak to close the schedule wasn’t obvious right away.
For most of the
season the Oilers have been a threat to beat any team any night, but are also
vulnerable to lesser opponents. They can beat the Avalanche 6-3 (with Mike
Smith) then lose to the Blue Jackets 5-2 just two nights later (with Koskinen).
Where Mikko paid back some of that money was on overs, but you didn’t want him
starting if you bet the under. My O/U algorithm was 8-4 recommending picks when
Smith was starting.
27) Nashville
Predators, (-$227):
Last Quarter Rank: 27
1st Quarter Profit: $199
2nd Quarter Profit: $321
3rd Quarter Profit: -$2,037
4th
Quarter Profit: $1,289
Q4 Win-Loss Record: 11-12
Q4 % Money Bet On: 35%
($17)
If you bet on them
every game ML+PL: -$783
(LR: 26)
Q4 % Money Bet Against: 65%
($168)
If you bet against
them every game ML+PL: $448 (LR: 7)
Q4 % Bet Over: 81% ($1,043),
Market Return on $1: $1.26
Q4 % Bet Under: 19% ($61),
Market Return on $1: $0.67
This might have been
my best quarter betting on Nashville Predators games since before Covid, with
most of the profit coming from overs, and to a lesser extent, losses. I’ll confess,
my betting them to lose had more to do with my lack of confidence in Juuse
Saros based on his erratic performance for my fantasy hockey teams down the
stretch (his SV% by quarter was .917, .930, .920, and .905). While it became
harder to reliably bet the Predators to win, Saros struggling in Q4 did juice
their overs, which helped them become one of my best O/U teams (overs went
15-8-0).
The Preds went 11-12
in Q4, and came within a few Vegas wins of missing the playoffs. One key stat,
they were terrible at covering pucklines -1.5 goals when favored, meaning their
opponents +1.5 goals was a winning wager. Though I rarely made that Preds PL bet myself,
having most of my success betting their opponents on the puckline. They
went 8-5 at home and 3-7 on the road; betting $100 on them to lose every road
moneyline netted you $527, but taking them to win every home ML only yielded
+$76 thanks to line price.
28) Toronto
Maple Leafs, (-$974):
Last Quarter Rank: 25
1st Quarter Profit: -$1,120
2nd Quarter Profit: $234
3rd Quarter Profit: $454
4th
Quarter Profit: -$542
Q4 Win-Loss Record: 17-6
Q4 % Money Bet On: 37%
(-$269)
If you bet on them
every game ML+PL: $986 (LR: 3)
Q4 % Money Bet Against: 63%
(-$1,054)
If you bet against
them every game ML+PL: -$1,384 (LR:
29)
Q4 % Bet Over: 83% ($907),
Market Return on $1: $1.18
Q4 % Bet Under: 17% (-$126),
Market Return on $1: $0.76
The Toronto Maple
Leafs won 65% of their games this season, but they were suspiciously bad when
my money was on them to win. In the 2nd half, they won 56% of the
games I bet them to win, and 72% when I bet them to lose. I laid far too much
money on their opponents in the 4th quarter, but reading my comment
column, most of that was a function of expensive line prices. There is also
part of my stubbornly superstitious goalie brain that feels like they are a
worse team when I’m invested in their wins. Most of my empirical evidence that
my bets can make them lose is playoff-based, and I’d love to think I played a
key role in knocking them out of the playoffs every year.
The one investment
that really paid off for me in the last three quarters was Toronto overs as
they averaged 7.3 goals per game. Both goaltenders Campbell and Mrazek missed
time with injury, forcing rookie Erik Kallgren to start the most games, and he
went 8-4 with an .888 SV%. Campbell was a better goalie but had a higher rate
of return on overs than Kallgren because the Leafs gave him an extra goal per
game of offensive support.
29) Ottawa
Senators, (-$2,133):
Last Quarter Rank: 29
1st Quarter Profit: $347
2nd Quarter Profit: -$2,733
3rd Quarter Profit: $413
4th
Quarter Profit: -$160
Q4 Win-Loss Record: 12-12
Q4 % Money Bet On: 32%
($362)
If you bet on them
every game ML+PL: $639 (LR: 4)
Q4 % Money Bet Against: 68%
(-$219)
If you bet against
them every game ML+PL: -$1,183 (LR:
28)
Q4 % Bet Over: 83% (-$83),
Market Return on $1: $1.01
Q4 % Bet Under: 17% (-$220),
Market Return on $1: $0.91
If you delete a
2-week span where the Senators were terrible then suddenly reversed course and defeated
some of the league’s best teams (including Tampa and Florida), I’ve been decent
betting Ottawa. While 68% of my money was invested in their losses, I generated
a respectable return picking Sens to win. In fact, they were the 4th best team in the NHL to bet to win every game in the fourth quarter, despite
going 12-12 (keep in mind, 17 of their 24 games were against teams that missed
the playoffs), so they weren’t exactly world-beaters.
One of the keys to
understanding Ottawa was the stark difference in the goaltending, with Anton
Forsberg going 9-7 with a .918 SV%, and the others going 3-5 with an .883. In
the final 3 quarters of the schedule, I lost -$3,829 betting Anton Forsberg to lose. I was
aware of this problem by the end of the 2nd half, but it’s hard to
course-correct when you often don’t know which goalie will start when you’re
making picks. Perhaps I could have put more effort into predicting which gatekeeper
would get the net every game. My O/U algorithm lost -$572 when Forsberg
started, and was +$269 in the other games.
30) Winnipeg
Jets, (-$2,410):
Last Quarter Rank: 30
1st Quarter Profit: -$274
2nd Quarter Profit: $101
3rd Quarter Profit: -$2,319
4th
Quarter Profit: $82
Q4 Win-Loss Record: 12-10
Q4 % Money Bet On: 26%
($454)
If you bet on them
every game ML+PL: -$545
(LR: 21)
Q4 % Money Bet Against: 74%
($787)
If you bet against
them every game ML+PL: $222 (LR: 12)
Q4 % Bet Over: 85% (-$659),
Market Return on $1: $0.89
Q4 % Bet Under: 15% (-$500),
Market Return on $1: $1.02
My Winnipeg Jets fourth
quarter results would have been far better if my algorithm had been any good at
picking their over/unders. Trying to predict their goal totals game-to-game has
been a struggle all season. They have a goalie who at his best is among the
elite of the elite, but was also inconsistent and unreliable. The offense too
would run red hot or ice cold. Goaltending wasn’t their problem in the 4th quarter, as Hellebuyck posted a .918 SV% and Eric Comrie had a .921. I’m not
sure why my algorithm recommended an 85% stake in overs when their unders were
a better bet (in all likelihood they faced high scoring opponents). I lost -$593 on Hellebuyck Q4 overs
specifically, adding to my -$2,416 of losses on Jets O/U this season;
which also means I was +$6 betting their wins and losses.
Speaking of wins and
losses, that’s where my Jets Q4 was a big success. 74% of my money was invested
in their losses, which produced a big return when they went on a 2-8 run at the
start of April. Had they not closed the schedule with a 4-game winning streak,
I would have been +$1,487 when betting them to lose (which would have ranked
them as my 2nd best team to bet against in Q4).
31) Pittsburgh
Penguins, (-$3,008):
Last Quarter Rank: 31
1st Quarter Profit: -$2,233
2nd Quarter Profit: $720
3rd Quarter Profit: -$1,531
4th
Quarter Profit: $35
Q4 Win-Loss Record: 10-12
Q4 % Money Bet On: 23%
($335)
If you bet on them
every game ML+PL: -$769
(LR: 25)
Q4 % Money Bet Against: 77%
($991)
If you bet against
them every game ML+PL: $265 (LR: 11)
Q4 % Bet Over: 47% (-$740),
Market Return on $1: $0.83
Q4 % Bet Under: 53% (-$550),
Market Return on $1: $1.08
My fourth quarter
betting Pittsburgh wins and losses was very successful, but they continued to
confound my over/under algorithm. Most of the revenue generated from Pittsburgh
in Q4 came from betting them to lose, or more specifically, betting Tristan Jarry
to lose. I ran a balance of +$1,017 in Jarry starts and -$1,092 in DeSmith starts.
DeSmith was the better goaltender, posting a .925 SV% versus .908 for Jarry;
but strangely I lost -$525 on DeSmith unders
and -$531 on Jarry overs. Part
of that was Jarry being the first string, so DeSmith was getting the call
against weaker opponents.
The Penguins did
struggle in Q4, going 10-12. Laying $100 on every opponent moneyline would have
netted you $425 of profit. That was my own recipe for Q4 success, Pittsburgh
opponent ML. They had been a strong road team for most the season, but that
dropped to 4-8 in the final quarter, while going 6-4 at home. Most of my wagers
on games in Pittsburgh were actually on their opponents to win, which produced
a solid return, but a lot of that had to do with a $500 bet on Colorado to win
as +100 underdogs, and a $250 bet on the Rangers at +145.
32) Los
Angeles Kings, ($8,535):
Last Quarter Rank: 32
1st Quarter Profit: -$1,943
2nd Quarter Profit: -$1,702
3rd Quarter Profit: -$3,322
4th
Quarter Profit: -$1,567
Q4 Win-Loss Record: 11-10
Q4 % Money Bet On: 37%
(-$216)
If you bet on them
every game ML+PL: $355 (LR: 9)
Q4 % Money Bet Against: 63%
(-$379)
If you bet against
them every game ML+PL: -$731 (LR:
25)
Q4 % Bet Over: 76% (-$850),
Market Return on $1: $0.83
Q4 % Bet Under: 24% (-$122),
Market Return on $1: $1.09
My struggles with the
LA Kings continued into the fourth quarter, even as I tried different abstract strategies
like picking the winner by flipping a coin. The coin was awful at selecting winners.
Everything failed. Betting them to win, lose, over, or under, all losers. I’ve
been aware of this problem all season and have been unable to correct it. In Q4,
they were 9-3 against teams below .500, and 2-7 against teams above .500. Beat
bad teams, lose to good teams would have been a winning strategy, but that’s
what I was doing earlier in the season when they were losing to bad teams and
beating good teams. My “do the opposite of what I think will happen” tactic led
me astray.
The fact that the
team has confounded my over/under algorithm was a big contributing factor.
Nearly half of all my Q4 money lost on the Kings came from Cal Petersen overs,
which is strange considering his SV% was .883. Problem was, the Kings averaged almost a
goal per game more when Jonathan Quick was starting, versus only 2.4 for
Petersen. It’s worth pointing out that 2/3 of those blown Petersen overs would
have hit or pushed if 1 more goal was scored. I might have missed on a lot of
those bets, but most of the misses were relatively small.
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